{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**数据探索**\n",
    "\n",
    "2、 数据说明:\n",
    "原始数据集地址: https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes\n",
    "数据集只有一个文件(diabetes.csv):Pima Indians Diabetes Dataset 包括根据医疗\n",
    "记录的比马印第安人 5 年内糖尿病的发病情况,这是一个两类分类问题。每个类的\n",
    "样本数目数量不均等。一共有 768 个样本,每个样本有 8 个输入变量和 1 个输出\n",
    "变量。缺失值通常用零值编码。\n",
    "\n",
    "数据集共9个字段: \n",
    "0列为pregnants(怀孕次数)；\n",
    "1列为glucose(口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度)；\n",
    "2列为blood_pressure(舒张压,单位:mm Hg）\n",
    "3列为skin_thickness(三头肌皮褶厚度,单位：mm）\n",
    "4列为insulin(餐后血清胰岛素,单位:mm）\n",
    "5列为BMI,体重指数（体重（公斤）/ 身高（米）^2）\n",
    "6列为Diabetes_pedigree_function(糖尿病家系作用)\n",
    "7列为Age(年龄)\n",
    "8列为Outcome(分类变量,0或1）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. import 工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#首先 import 必要的模块\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train :\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据基本信息\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集存在缺失值，缺失值被标记为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果中我们可以看到很多列的最小值为0。而在1-5列代表的变量中，0值并没有意义，这就表名该值无效或为缺失值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第1、2、5列中0值较少；第3、4列中的0值很多，接近总量768的一半。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. 查看每个变量与标签之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1）标签Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Outcome'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2）怀孕次数Pregnancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "sns.countplot(train['Pregnancies'])\n",
    "plt.xlabel('Number of Pregnancies')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数17次？但在疾病判断案例中，异常值可能就意味着得病，不能删除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3)血浆葡萄糖浓度Glucose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Glucose, kde = True)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Glucose', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Glucose', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4)舒张压BloodPressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BloodPressure, kde = True)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "血压为0为缺失值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BloodPressure', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BloodPressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5)三头肌皮褶厚度SkinThickness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.SkinThickness, kde = True)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='SkinThickness', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('SkinThickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6)餐后血清胰岛素Insulin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Insulin, kde = True)\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Insulin', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7)体重指数BMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI, kde = True)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BMI', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8)糖尿病家系作用DiabetesPedigreeFunction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.DiabetesPedigreeFunction, kde = True)\n",
    "plt.xlabel('DiabetesPedigreeFunction')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='DiabetesPedigreeFunction', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('DiabetesPedigreeFunction', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9)年龄Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Age, kde = True)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jhony/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Age', data=train, hue=\"Outcome\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Age', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2700555c50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  }
 ],
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